Other methods have been proposed to learn large scale features.
Pooling layers increase the receptive field size in CNNs, but they lose a significant amount of information, including the spatial relationship between features [1]}.
Multiscale pyramids use kernels of different sizes arranged in parallel, but larger kernels have lower resolution in order to keep a reasonable number of parameters [2]}.
Deformable convolutions adaptively learn the shape of the kernel, at the cost of an increased complexity of the model [3]}.
Dilated convolutions increase the kernel size along network depth [4]} and they are equivalent to a tensor decomposition [5]}.
Tensor decompositions have been widely used to reduce the complexity of convolutional and dense layers [6]}, [7]}, [8]}.
We show below that one cycle of CycleNet is equivalent to a decomposition of a dense layer, in the simple case of \(1\times 1\)  convolutions and linear activations, but is different in the general case.
CycleNet is also different from a 3D convolution [9]}, since spatial coordinates are fully connected in the second and third layers of a cycle.
